Predictive Structure-based Guidelines for Identifying Optimal PEGylation Sites within Proteins

用于识别蛋白质内最佳聚乙二醇化位点的基于预测结构的指南

• 批准号: 8958215
• 负责人: Joshua L Price
• 金额: $ 34.56万
• 依托单位: BRIGHAM YOUNG UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8958215
• 关键词: Antibodies; Biological; Chickens; Development; Drug Kinetics; Figs - dietary; Goals; Granulocyte Colony-Stimulating Factor; Guidelines; Health; Human; Immunoglobulin G; Interferons; Lead; Learning; Length; Location; Maleimides; Modeling; Molecular; Peptides; Pharmaceutical Preparations; Pin1 protein; Polyethylene Glycols; Polymers; Positioning Attribute; Property; Proteins; Proteolysis; Relative (related person); SH3 Domains; Side; Site; Site-Directed Mutagenesis; Solvents; Structure; Techniques; Therapeutic; Triad Acrylic Resin; Work; base; molecular dynamics; protein folding; protein misfolding; public health relevance; therapeutic protein; tool

 DESCRIPTION (provided by applicant): Our goal is to develop structure-based tools for identifying optimal PEGylation sites within peptides/proteins and to use these tools to enhance peptide/protein pharmacokinetic properties. Our central hypothesis is that optimal PEGylation sites should be characterized by the ability of the attached PEG to enhance peptide/protein conformational stability. Our rationale for this hypothesis is that unstable, unfolded or misfolded proteins tend to be non-functional and have more pharmacokinetic problems than folded proteins (i.e., are more aggregation-prone, more susceptible to proteolysis, and more readily recognized by antibodies). Therefore increases in protein conformational stability should also enhance protein pharmacokinetic properties However, current PEGylation efforts lack predictive tools for increasing protein stability; instead, a trial-and-error approach prevails, which frequenly results in diminished biological activity relative to the non-PEGylated protein. Using our growing molecular-level understanding of PEG-based protein stabilization, we will develop predictive structure-based tools for generating PEGylated peptides/proteins with enhanced pharmacokinetic properties and undiminished function, thereby accelerating the development of better PEGylated protein drugs.

 描述（由应用程序提供）：我们的目标是开发基于结构的工具来识别宠物/蛋白质中的最佳卵巢位点，并使用这些工具来增强肽/蛋白质药代动力学特性。我们的中心假设是，最佳的卵巢位点的特征在于附着的PEG增强肽/蛋白质构象稳定性的能力。我们对此假设的理由是不稳定，展开或错误折叠 蛋白质往往是非功能性的，并且比折叠蛋白（即更容易聚集，更容易受到蛋白质解体，并且更容易被抗体识别）。因此，蛋白质构象稳定性的提高还应增强蛋白质药代动力学特性，但是，当前的Pegylation努力缺乏提高蛋白质稳定性的预测工具。取而代之的是，试验方法占上风，这通常导致相对于非垂脉蛋白的生物学活性减少。利用我们对基于PEG的蛋白质稳定化的分子水平的理解，我们将开发基于预测的结构工具，以产生具有增强的药代动力学特性和不太减少功能的卵巢肽/蛋白质，从而加速了更好的pegyped蛋白质药物的发展。